One important approach to attaining generalization across states is referred to as function approximation [1]}, which attempts to assign similar representations to states in which similar actions are required. In one simple implementation of this approach, called linear function approximation, each state or situation is encoded as a set of features, and the learner uses a linear readout of these as a basis for selecting its actions.
